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This allows users to specify a kernel from a number of pre-defined kernels, or to fully specify their own kernel for a specific Convolution or Morphology Operation.</p> 60 61<p>The kernel so generated can be any rectangular array of floating point values (doubles) with the 'control point' or 'pixel being affected' anywhere within that array of values.</p> 62 63<p>Previously IM was restricted to a square of odd size using the exact center as origin, this is no longer the case, and any rectangular kernel with any value being declared the origin. This in turn allows the use of highly asymmetrical kernels.</p> 64 65<p>The floating point values in the kernel can also include a special value known as 'nan' or 'not a number' to indicate that this value is not part of the kernel array. This allows you to shaped the kernel within its rectangular area. That is 'nan' values provide a 'mask' for the kernel shape. However at least one non-nan value must be provided for correct working of a kernel.</p> 66 67<p>The returned kernel should be freed using the DestroyKernelInfo() when you are finished with it. Do not free this memory yourself.</p> 68 69<p>Input kernel defintion strings can consist of any of three types.</p> 70 71<p>"name:args[[@><]" Select from one of the built in kernels, using the name and geometry arguments supplied. See AcquireKernelBuiltIn()</p> 72 73<p>"WxH[+X+Y][@><]:num, num, num ..." a kernel of size W by H, with W*H floating point numbers following. the 'center' can be optionally be defined at +X+Y (such that +0+0 is top left corner). If not defined the pixel in the center, for odd sizes, or to the immediate top or left of center for even sizes is automatically selected.</p> 74 75<p>"num, num, num, num, ..." list of floating point numbers defining an 'old style' odd sized square kernel. At least 9 values should be provided for a 3x3 square kernel, 25 for a 5x5 square kernel, 49 for 7x7, etc. Values can be space or comma separated. This is not recommended.</p> 76 77<p>You can define a 'list of kernels' which can be used by some morphology operators A list is defined as a semi-colon separated list kernels.</p> 78 79<p>" kernel ; kernel ; kernel ; "</p> 80 81<p>Any extra ';' characters, at start, end or between kernel defintions are simply ignored.</p> 82 83<p>The special flags will expand a single kernel, into a list of rotated kernels. A '@' flag will expand a 3x3 kernel into a list of 45-degree cyclic rotations, while a '>' will generate a list of 90-degree rotations. The '<' also exands using 90-degree rotates, but giving a 180-degree reflected kernel before the +/- 90-degree rotations, which can be important for Thinning operations.</p> 84 85<p>Note that 'name' kernels will start with an alphabetic character while the new kernel specification has a ':' character in its specification string. If neither is the case, it is assumed an old style of a simple list of numbers generating a odd-sized square kernel has been given.</p> 86 87<p>The format of the AcquireKernal method is:</p> 88 89<pre class="text"> 90KernelInfo *AcquireKernelInfo(const char *kernel_string) 91</pre> 92 93<p>A description of each parameter follows:</p> 94 95<dd> 96</dd> 97 98<dd> </dd> 99<dl class="dl-horizontal"> 100<dt>kernel_string</dt> 101<dd>the Morphology/Convolution kernel wanted. </dd> 102 103<dd> </dd> 104</dl> 105<h2><a href="http://nextgen.imagemagick.org/api/MagickCore/morphology_8c.html" id="AcquireKernelBuiltIn">AcquireKernelBuiltIn</a></h2> 106 107<p>AcquireKernelBuiltIn() returned one of the 'named' built-in types of kernels used for special purposes such as gaussian blurring, skeleton pruning, and edge distance determination.</p> 108 109<p>They take a KernelType, and a set of geometry style arguments, which were typically decoded from a user supplied string, or from a more complex Morphology Method that was requested.</p> 110 111<p>The format of the AcquireKernalBuiltIn method is:</p> 112 113<pre class="text"> 114KernelInfo *AcquireKernelBuiltIn(const KernelInfoType type, 115 const GeometryInfo args) 116</pre> 117 118<p>A description of each parameter follows:</p> 119 120<dd> 121</dd> 122 123<dd> </dd> 124<dl class="dl-horizontal"> 125<dt>type</dt> 126<dd>the pre-defined type of kernel wanted </dd> 127 128<dd> </dd> 129<dt>args</dt> 130<dd>arguments defining or modifying the kernel </dd> 131 132<dd> Convolution Kernels </dd> 133 134<dd> Unity The a No-Op or Scaling single element kernel. </dd> 135 136<dd> Gaussian:{radius},{sigma} Generate a two-dimensional gaussian kernel, as used by -gaussian. The sigma for the curve is required. The resulting kernel is normalized, </dd> 137 138<dd> If 'sigma' is zero, you get a single pixel on a field of zeros. </dd> 139 140<dd> NOTE: that the 'radius' is optional, but if provided can limit (clip) the final size of the resulting kernel to a square 2*radius+1 in size. The radius should be at least 2 times that of the sigma value, or sever clipping and aliasing may result. If not given or set to 0 the radius will be determined so as to produce the best minimal error result, which is usally much larger than is normally needed. </dd> 141 142<dd> LoG:{radius},{sigma} "Laplacian of a Gaussian" or "Mexician Hat" Kernel. The supposed ideal edge detection, zero-summing kernel. </dd> 143 144<dd> An alturnative to this kernel is to use a "DoG" with a sigma ratio of approx 1.6 (according to wikipedia). </dd> 145 146<dd> DoG:{radius},{sigma1},{sigma2} "Difference of Gaussians" Kernel. As "Gaussian" but with a gaussian produced by 'sigma2' subtracted from the gaussian produced by 'sigma1'. Typically sigma2 > sigma1. The result is a zero-summing kernel. </dd> 147 148<dd> Blur:{radius},{sigma}[,{angle}] Generates a 1 dimensional or linear gaussian blur, at the angle given (current restricted to orthogonal angles). If a 'radius' is given the kernel is clipped to a width of 2*radius+1. Kernel can be rotated by a 90 degree angle. </dd> 149 150<dd> If 'sigma' is zero, you get a single pixel on a field of zeros. </dd> 151 152<dd> Note that two convolutions with two "Blur" kernels perpendicular to each other, is equivalent to a far larger "Gaussian" kernel with the same sigma value, However it is much faster to apply. This is how the "-blur" operator actually works. </dd> 153 154<dd> Comet:{width},{sigma},{angle} Blur in one direction only, much like how a bright object leaves a comet like trail. The Kernel is actually half a gaussian curve, Adding two such blurs in opposite directions produces a Blur Kernel. Angle can be rotated in multiples of 90 degrees. </dd> 155 156<dd> Note that the first argument is the width of the kernel and not the radius of the kernel. </dd> 157 158<dd> Binomial:[{radius}] Generate a discrete kernel using a 2 dimentional Pascel's Triangle of values. Used for special forma of image filters. </dd> 159 160<dd> # Still to be implemented... # # Filter2D # Filter1D # Set kernel values using a resize filter, and given scale (sigma) # Cylindrical or Linear. Is this possible with an image? # </dd> 161 162<dd> Named Constant Convolution Kernels </dd> 163 164<dd> All these are unscaled, zero-summing kernels by default. As such for non-HDRI version of ImageMagick some form of normalization, user scaling, and biasing the results is recommended, to prevent the resulting image being 'clipped'. </dd> 165 166<dd> The 3x3 kernels (most of these) can be circularly rotated in multiples of 45 degrees to generate the 8 angled varients of each of the kernels. </dd> 167 168<dd> Laplacian:{type} Discrete Lapacian Kernels, (without normalization) Type 0 : 3x3 with center:8 surounded by -1 (8 neighbourhood) Type 1 : 3x3 with center:4 edge:-1 corner:0 (4 neighbourhood) Type 2 : 3x3 with center:4 edge:1 corner:-2 Type 3 : 3x3 with center:4 edge:-2 corner:1 Type 5 : 5x5 laplacian Type 7 : 7x7 laplacian Type 15 : 5x5 LoG (sigma approx 1.4) Type 19 : 9x9 LoG (sigma approx 1.4) </dd> 169 170<dd> Sobel:{angle} Sobel 'Edge' convolution kernel (3x3) | -1, 0, 1 | | -2, 0,-2 | | -1, 0, 1 | </dd> 171 172<dd> Roberts:{angle} Roberts convolution kernel (3x3) | 0, 0, 0 | | -1, 1, 0 | | 0, 0, 0 | </dd> 173 174<dd> Prewitt:{angle} Prewitt Edge convolution kernel (3x3) | -1, 0, 1 | | -1, 0, 1 | | -1, 0, 1 | </dd> 175 176<dd> Compass:{angle} Prewitt's "Compass" convolution kernel (3x3) | -1, 1, 1 | | -1,-2, 1 | | -1, 1, 1 | </dd> 177 178<dd> Kirsch:{angle} Kirsch's "Compass" convolution kernel (3x3) | -3,-3, 5 | | -3, 0, 5 | | -3,-3, 5 | </dd> 179 180<dd> FreiChen:{angle} Frei-Chen Edge Detector is based on a kernel that is similar to the Sobel Kernel, but is designed to be isotropic. That is it takes into account the distance of the diagonal in the kernel. </dd> 181 182<dd> | 1, 0, -1 | | sqrt(2), 0, -sqrt(2) | | 1, 0, -1 | </dd> 183 184<dd> FreiChen:{type},{angle} </dd> 185 186<dd> Frei-Chen Pre-weighted kernels... </dd> 187 188<dd> Type 0: default un-nomalized version shown above. </dd> 189 190<dd> Type 1: Orthogonal Kernel (same as type 11 below) | 1, 0, -1 | | sqrt(2), 0, -sqrt(2) | / 2*sqrt(2) | 1, 0, -1 | </dd> 191 192<dd> Type 2: Diagonal form of Kernel... | 1, sqrt(2), 0 | | sqrt(2), 0, -sqrt(2) | / 2*sqrt(2) | 0, -sqrt(2) -1 | </dd> 193 194<dd> However this kernel is als at the heart of the FreiChen Edge Detection Process which uses a set of 9 specially weighted kernel. These 9 kernels not be normalized, but directly applied to the image. The results is then added together, to produce the intensity of an edge in a specific direction. The square root of the pixel value can then be taken as the cosine of the edge, and at least 2 such runs at 90 degrees from each other, both the direction and the strength of the edge can be determined. </dd> 195 196<dd> Type 10: All 9 of the following pre-weighted kernels... </dd> 197 198<dd> Type 11: | 1, 0, -1 | | sqrt(2), 0, -sqrt(2) | / 2*sqrt(2) | 1, 0, -1 | </dd> 199 200<dd> Type 12: | 1, sqrt(2), 1 | | 0, 0, 0 | / 2*sqrt(2) | 1, sqrt(2), 1 | </dd> 201 202<dd> Type 13: | sqrt(2), -1, 0 | | -1, 0, 1 | / 2*sqrt(2) | 0, 1, -sqrt(2) | </dd> 203 204<dd> Type 14: | 0, 1, -sqrt(2) | | -1, 0, 1 | / 2*sqrt(2) | sqrt(2), -1, 0 | </dd> 205 206<dd> Type 15: | 0, -1, 0 | | 1, 0, 1 | / 2 | 0, -1, 0 | </dd> 207 208<dd> Type 16: | 1, 0, -1 | | 0, 0, 0 | / 2 | -1, 0, 1 | </dd> 209 210<dd> Type 17: | 1, -2, 1 | | -2, 4, -2 | / 6 | -1, -2, 1 | </dd> 211 212<dd> Type 18: | -2, 1, -2 | | 1, 4, 1 | / 6 | -2, 1, -2 | </dd> 213 214<dd> Type 19: | 1, 1, 1 | | 1, 1, 1 | / 3 | 1, 1, 1 | </dd> 215 216<dd> The first 4 are for edge detection, the next 4 are for line detection and the last is to add a average component to the results. </dd> 217 218<dd> Using a special type of '-1' will return all 9 pre-weighted kernels as a multi-kernel list, so that you can use them directly (without normalization) with the special "-set option:morphology:compose Plus" setting to apply the full FreiChen Edge Detection Technique. </dd> 219 220<dd> If 'type' is large it will be taken to be an actual rotation angle for the default FreiChen (type 0) kernel. As such FreiChen:45 will look like a Sobel:45 but with 'sqrt(2)' instead of '2' values. </dd> 221 222<dd> WARNING: The above was layed out as per http://www.math.tau.ac.il/~turkel/notes/edge_detectors.pdf But rotated 90 degrees so direction is from left rather than the top. I have yet to find any secondary confirmation of the above. The only other source found was actual source code at http://ltswww.epfl.ch/~courstiv/exos_labos/sol3.pdf Neigher paper defineds the kernels in a way that looks locical or correct when taken as a whole. </dd> 223 224<dd> Boolean Kernels </dd> 225 226<dd> Diamond:[{radius}[,{scale}]] Generate a diamond shaped kernel with given radius to the points. Kernel size will again be radius*2+1 square and defaults to radius 1, generating a 3x3 kernel that is slightly larger than a square. </dd> 227 228<dd> Square:[{radius}[,{scale}]] Generate a square shaped kernel of size radius*2+1, and defaulting to a 3x3 (radius 1). </dd> 229 230<dd> Octagon:[{radius}[,{scale}]] Generate octagonal shaped kernel of given radius and constant scale. Default radius is 3 producing a 7x7 kernel. A radius of 1 will result in "Diamond" kernel. </dd> 231 232<dd> Disk:[{radius}[,{scale}]] Generate a binary disk, thresholded at the radius given, the radius may be a float-point value. Final Kernel size is floor(radius)*2+1 square. A radius of 5.3 is the default. </dd> 233 234<dd> NOTE: That a low radii Disk kernels produce the same results as many of the previously defined kernels, but differ greatly at larger radii. Here is a table of equivalences... "Disk:1" => "Diamond", "Octagon:1", or "Cross:1" "Disk:1.5" => "Square" "Disk:2" => "Diamond:2" "Disk:2.5" => "Octagon" "Disk:2.9" => "Square:2" "Disk:3.5" => "Octagon:3" "Disk:4.5" => "Octagon:4" "Disk:5.4" => "Octagon:5" "Disk:6.4" => "Octagon:6" All other Disk shapes are unique to this kernel, but because a "Disk" is more circular when using a larger radius, using a larger radius is preferred over iterating the morphological operation. </dd> 235 236<dd> Rectangle:{geometry} Simply generate a rectangle of 1's with the size given. You can also specify the location of the 'control point', otherwise the closest pixel to the center of the rectangle is selected. </dd> 237 238<dd> Properly centered and odd sized rectangles work the best. </dd> 239 240<dd> Symbol Dilation Kernels </dd> 241 242<dd> These kernel is not a good general morphological kernel, but is used more for highlighting and marking any single pixels in an image using, a "Dilate" method as appropriate. </dd> 243 244<dd> For the same reasons iterating these kernels does not produce the same result as using a larger radius for the symbol. </dd> 245 246<dd> Plus:[{radius}[,{scale}]] Cross:[{radius}[,{scale}]] Generate a kernel in the shape of a 'plus' or a 'cross' with a each arm the length of the given radius (default 2). </dd> 247 248<dd> NOTE: "plus:1" is equivalent to a "Diamond" kernel. </dd> 249 250<dd> Ring:{radius1},{radius2}[,{scale}] A ring of the values given that falls between the two radii. Defaults to a ring of approximataly 3 radius in a 7x7 kernel. This is the 'edge' pixels of the default "Disk" kernel, More specifically, "Ring" -> "Ring:2.5,3.5,1.0" </dd> 251 252<dd> Hit and Miss Kernels </dd> 253 254<dd> Peak:radius1,radius2 Find any peak larger than the pixels the fall between the two radii. The default ring of pixels is as per "Ring". Edges Find flat orthogonal edges of a binary shape Corners Find 90 degree corners of a binary shape Diagonals:type A special kernel to thin the 'outside' of diagonals LineEnds:type Find end points of lines (for pruning a skeletion) Two types of lines ends (default to both) can be searched for Type 0: All line ends Type 1: single kernel for 4-conneected line ends Type 2: single kernel for simple line ends LineJunctions Find three line junctions (within a skeletion) Type 0: all line junctions Type 1: Y Junction kernel Type 2: Diagonal T Junction kernel Type 3: Orthogonal T Junction kernel Type 4: Diagonal X Junction kernel Type 5: Orthogonal + Junction kernel Ridges:type Find single pixel ridges or thin lines Type 1: Fine single pixel thick lines and ridges Type 2: Find two pixel thick lines and ridges ConvexHull Octagonal Thickening Kernel, to generate convex hulls of 45 degrees Skeleton:type Traditional skeleton generating kernels. Type 1: Tradional Skeleton kernel (4 connected skeleton) Type 2: HIPR2 Skeleton kernel (8 connected skeleton) Type 3: Thinning skeleton based on a ressearch paper by Dan S. Bloomberg (Default Type) ThinSE:type A huge variety of Thinning Kernels designed to preserve conectivity. many other kernel sets use these kernels as source definitions. Type numbers are 41-49, 81-89, 481, and 482 which are based on the super and sub notations used in the source research paper. </dd> 255 256<dd> Distance Measuring Kernels </dd> 257 258<dd> Different types of distance measuring methods, which are used with the a 'Distance' morphology method for generating a gradient based on distance from an edge of a binary shape, though there is a technique for handling a anti-aliased shape. </dd> 259 260<dd> See the 'Distance' Morphological Method, for information of how it is applied. </dd> 261 262<dd> Chebyshev:[{radius}][x{scale}[!]] Chebyshev Distance (also known as Tchebychev or Chessboard distance) is a value of one to any neighbour, orthogonal or diagonal. One why of thinking of it is the number of squares a 'King' or 'Queen' in chess needs to traverse reach any other position on a chess board. It results in a 'square' like distance function, but one where diagonals are given a value that is closer than expected. </dd> 263 264<dd> Manhattan:[{radius}][x{scale}[!]] Manhattan Distance (also known as Rectilinear, City Block, or the Taxi Cab distance metric), it is the distance needed when you can only travel in horizontal or vertical directions only. It is the distance a 'Rook' in chess would have to travel, and results in a diamond like distances, where diagonals are further than expected. </dd> 265 266<dd> Octagonal:[{radius}][x{scale}[!]] An interleving of Manhatten and Chebyshev metrics producing an increasing octagonally shaped distance. Distances matches those of the "Octagon" shaped kernel of the same radius. The minimum radius and default is 2, producing a 5x5 kernel. </dd> 267 268<dd> Euclidean:[{radius}][x{scale}[!]] Euclidean distance is the 'direct' or 'as the crow flys' distance. However by default the kernel size only has a radius of 1, which limits the distance to 'Knight' like moves, with only orthogonal and diagonal measurements being correct. As such for the default kernel you will get octagonal like distance function. </dd> 269 270<dd> However using a larger radius such as "Euclidean:4" you will get a much smoother distance gradient from the edge of the shape. Especially if the image is pre-processed to include any anti-aliasing pixels. Of course a larger kernel is slower to use, and not always needed. </dd> 271 272<dd> The first three Distance Measuring Kernels will only generate distances of exact multiples of {scale} in binary images. As such you can use a scale of 1 without loosing any information. However you also need some scaling when handling non-binary anti-aliased shapes. </dd> 273 274<dd> The "Euclidean" Distance Kernel however does generate a non-integer fractional results, and as such scaling is vital even for binary shapes. </dd> 275 276<dd> </dd> 277</dl> 278<h2><a href="http://nextgen.imagemagick.org/api/MagickCore/morphology_8c.html" id="CloneKernelInfo">CloneKernelInfo</a></h2> 279 280<p>CloneKernelInfo() creates a new clone of the given Kernel List so that its can be modified without effecting the original. The cloned kernel should be destroyed using DestoryKernelInfo() when no longer needed.</p> 281 282<p>The format of the CloneKernelInfo method is:</p> 283 284<pre class="text"> 285KernelInfo *CloneKernelInfo(const KernelInfo *kernel) 286</pre> 287 288<p>A description of each parameter follows:</p> 289 290<dd> 291</dd> 292 293<dd> </dd> 294<dl class="dl-horizontal"> 295<dt>kernel</dt> 296<dd>the Morphology/Convolution kernel to be cloned </dd> 297 298<dd> </dd> 299</dl> 300<h2><a href="http://nextgen.imagemagick.org/api/MagickCore/morphology_8c.html" id="DestroyKernelInfo">DestroyKernelInfo</a></h2> 301 302<p>DestroyKernelInfo() frees the memory used by a Convolution/Morphology kernel.</p> 303 304<p>The format of the DestroyKernelInfo method is:</p> 305 306<pre class="text"> 307KernelInfo *DestroyKernelInfo(KernelInfo *kernel) 308</pre> 309 310<p>A description of each parameter follows:</p> 311 312<dd> 313</dd> 314 315<dd> </dd> 316<dl class="dl-horizontal"> 317<dt>kernel</dt> 318<dd>the Morphology/Convolution kernel to be destroyed </dd> 319 320<dd> </dd> 321</dl> 322<h2><a href="http://nextgen.imagemagick.org/api/MagickCore/morphology_8c.html" id="MorphologyApply">MorphologyApply</a></h2> 323 324<p>MorphologyApply() applies a morphological method, multiple times using a list of multiple kernels. This is the method that should be called by other 'operators' that internally use morphology operations as part of their processing.</p> 325 326<p>It is basically equivalent to as MorphologyImage() (see below) but without any user controls. This allows internel programs to use this method to perform a specific task without possible interference by any API user supplied settings.</p> 327 328<p>It is MorphologyImage() task to extract any such user controls, and pass them to this function for processing.</p> 329 330<p>More specifically all given kernels should already be scaled, normalised, and blended appropriatally before being parred to this routine. The appropriate bias, and compose (typically 'UndefinedComposeOp') given.</p> 331 332<p>The format of the MorphologyApply method is:</p> 333 334<pre class="text"> 335Image *MorphologyApply(const Image *image,MorphologyMethod method, 336 const ssize_t iterations,const KernelInfo *kernel, 337 const CompositeMethod compose,const double bias, 338 ExceptionInfo *exception) 339</pre> 340 341<p>A description of each parameter follows:</p> 342 343<dd> 344</dd> 345 346<dd> </dd> 347<dl class="dl-horizontal"> 348<dt>image</dt> 349<dd>the source image </dd> 350 351<dd> </dd> 352<dt>method</dt> 353<dd>the morphology method to be applied. </dd> 354 355<dd> </dd> 356<dt>iterations</dt> 357<dd>apply the operation this many times (or no change). A value of -1 means loop until no change found. How this is applied may depend on the morphology method. Typically this is a value of 1. </dd> 358 359<dd> </dd> 360<dt>channel</dt> 361<dd>the channel type. </dd> 362 363<dd> </dd> 364<dt>kernel</dt> 365<dd>An array of double representing the morphology kernel. </dd> 366 367<dd> </dd> 368<dt>compose</dt> 369<dd>How to handle or merge multi-kernel results. If 'UndefinedCompositeOp' use default for the Morphology method. If 'NoCompositeOp' force image to be re-iterated by each kernel. Otherwise merge the results using the compose method given. </dd> 370 371<dd> </dd> 372<dt>bias</dt> 373<dd>Convolution Output Bias. </dd> 374 375<dd> </dd> 376<dt>exception</dt> 377<dd>return any errors or warnings in this structure. </dd> 378 379<dd> </dd> 380</dl> 381<h2><a href="http://nextgen.imagemagick.org/api/MagickCore/morphology_8c.html" id="This_is almost identical to the MorphologyPrimative">This is almost identical to the MorphologyPrimative</a></h2> 382 383<p>This is almost identical to the MorphologyPrimative() function above, but applies the primitive directly to the actual image using two passes, once in each direction, with the results of the previous (and current) row being re-used.</p> 384 385<p>That is after each row is 'Sync'ed' into the image, the next row makes use of those values as part of the calculation of the next row. It repeats, but going in the oppisite (bottom-up) direction.</p> 386 387<p>Because of this 're-use of results' this function can not make use of multi- threaded, parellel processing. </p> 388<h2><a href="http://nextgen.imagemagick.org/api/MagickCore/morphology_8c.html" id="MorphologyImage">MorphologyImage</a></h2> 389 390<p>MorphologyImage() applies a user supplied kernel to the image according to the given mophology method.</p> 391 392<p>This function applies any and all user defined settings before calling the above internal function MorphologyApply().</p> 393 394<p>User defined settings include... * Output Bias for Convolution and correlation ("-define convolve:bias=??") * Kernel Scale/normalize settings ("-define convolve:scale=??") This can also includes the addition of a scaled unity kernel. * Show Kernel being applied ("-define morphology:showkernel=1")</p> 395 396<p>Other operators that do not want user supplied options interfering, especially "convolve:bias" and "morphology:showkernel" should use MorphologyApply() directly.</p> 397 398<p>The format of the MorphologyImage method is:</p> 399 400<pre class="text"> 401Image *MorphologyImage(const Image *image,MorphologyMethod method, 402 const ssize_t iterations,KernelInfo *kernel,ExceptionInfo *exception) 403</pre> 404 405<p>A description of each parameter follows:</p> 406 407<dd> 408</dd> 409 410<dd> </dd> 411<dl class="dl-horizontal"> 412<dt>image</dt> 413<dd>the image. </dd> 414 415<dd> </dd> 416<dt>method</dt> 417<dd>the morphology method to be applied. </dd> 418 419<dd> </dd> 420<dt>iterations</dt> 421<dd>apply the operation this many times (or no change). A value of -1 means loop until no change found. How this is applied may depend on the morphology method. Typically this is a value of 1. </dd> 422 423<dd> </dd> 424<dt>kernel</dt> 425<dd>An array of double representing the morphology kernel. Warning: kernel may be normalized for the Convolve method. </dd> 426 427<dd> </dd> 428<dt>exception</dt> 429<dd>return any errors or warnings in this structure. </dd> 430 431<dd> </dd> 432</dl> 433<h2><a href="http://nextgen.imagemagick.org/api/MagickCore/morphology_8c.html" id="ScaleGeometryKernelInfo">ScaleGeometryKernelInfo</a></h2> 434 435<p>ScaleGeometryKernelInfo() takes a geometry argument string, typically provided as a "-set option:convolve:scale {geometry}" user setting, and modifies the kernel according to the parsed arguments of that setting.</p> 436 437<p>The first argument (and any normalization flags) are passed to ScaleKernelInfo() to scale/normalize the kernel. The second argument is then passed to UnityAddKernelInfo() to add a scled unity kernel into the scaled/normalized kernel.</p> 438 439<p>The format of the ScaleGeometryKernelInfo method is:</p> 440 441<pre class="text"> 442void ScaleGeometryKernelInfo(KernelInfo *kernel, 443 const double scaling_factor,const MagickStatusType normalize_flags) 444</pre> 445 446<p>A description of each parameter follows:</p> 447 448<dd> 449</dd> 450 451<dd> </dd> 452<dl class="dl-horizontal"> 453<dt>kernel</dt> 454<dd>the Morphology/Convolution kernel to modify </dd> 455 456<dd> o geometry: </dd> 457 458<pre class="text"> 459 "-set option:convolve:scale {geometry}" setting. 460</pre> 461 462<p></dd> 463</dl> 464<h2><a href="http://nextgen.imagemagick.org/api/MagickCore/morphology_8c.html" id="ScaleKernelInfo">ScaleKernelInfo</a></h2> 465 466<p>ScaleKernelInfo() scales the given kernel list by the given amount, with or without normalization of the sum of the kernel values (as per given flags).</p> 467 468<p>By default (no flags given) the values within the kernel is scaled directly using given scaling factor without change.</p> 469 470<p>If either of the two 'normalize_flags' are given the kernel will first be normalized and then further scaled by the scaling factor value given.</p> 471 472<p>Kernel normalization ('normalize_flags' given) is designed to ensure that any use of the kernel scaling factor with 'Convolve' or 'Correlate' morphology methods will fall into -1.0 to +1.0 range. Note that for non-HDRI versions of IM this may cause images to have any negative results clipped, unless some 'bias' is used.</p> 473 474<p>More specifically. Kernels which only contain positive values (such as a 'Gaussian' kernel) will be scaled so that those values sum to +1.0, ensuring a 0.0 to +1.0 output range for non-HDRI images.</p> 475 476<p>For Kernels that contain some negative values, (such as 'Sharpen' kernels) the kernel will be scaled by the absolute of the sum of kernel values, so that it will generally fall within the +/- 1.0 range.</p> 477 478<p>For kernels whose values sum to zero, (such as 'Laplician' kernels) kernel will be scaled by just the sum of the postive values, so that its output range will again fall into the +/- 1.0 range.</p> 479 480<p>For special kernels designed for locating shapes using 'Correlate', (often only containing +1 and -1 values, representing foreground/brackground matching) a special normalization method is provided to scale the positive values separately to those of the negative values, so the kernel will be forced to become a zero-sum kernel better suited to such searches.</p> 481 482<p>WARNING: Correct normalization of the kernel assumes that the '*_range' attributes within the kernel structure have been correctly set during the kernels creation.</p> 483 484<p>NOTE: The values used for 'normalize_flags' have been selected specifically to match the use of geometry options, so that '!' means NormalizeValue, '^' means CorrelateNormalizeValue. All other GeometryFlags values are ignored.</p> 485 486<p>The format of the ScaleKernelInfo method is:</p> 487 488<pre class="text"> 489void ScaleKernelInfo(KernelInfo *kernel, const double scaling_factor, 490 const MagickStatusType normalize_flags ) 491</pre> 492 493<p>A description of each parameter follows:</p> 494 495<dd> 496</dd> 497 498<dd> </dd> 499<dl class="dl-horizontal"> 500<dt>kernel</dt> 501<dd>the Morphology/Convolution kernel </dd> 502 503<dd> o scaling_factor: </dd> 504 505<pre class="text"> 506 zero. If the kernel is normalized regardless of any flags. 507</pre> 508 509<p>o normalize_flags: </dd> 510 511<pre class="text"> 512 specifically: NormalizeValue, CorrelateNormalizeValue, 513 and/or PercentValue 514</pre> 515 516<p></dd> 517</dl> 518<h2><a href="http://nextgen.imagemagick.org/api/MagickCore/morphology_8c.html" id="ShowKernelInfo">ShowKernelInfo</a></h2> 519 520<p>ShowKernelInfo() outputs the details of the given kernel defination to standard error, generally due to a users 'morphology:showkernel' option request.</p> 521 522<p>The format of the ShowKernel method is:</p> 523 524<pre class="text"> 525void ShowKernelInfo(const KernelInfo *kernel) 526</pre> 527 528<p>A description of each parameter follows:</p> 529 530<dd> 531</dd> 532 533<dd> </dd> 534<dl class="dl-horizontal"> 535<dt>kernel</dt> 536<dd>the Morphology/Convolution kernel </dd> 537 538<dd> </dd> 539</dl> 540<h2><a href="http://nextgen.imagemagick.org/api/MagickCore/morphology_8c.html" id="UnityAddKernelInfo">UnityAddKernelInfo</a></h2> 541 542<p>UnityAddKernelInfo() Adds a given amount of the 'Unity' Convolution Kernel to the given pre-scaled and normalized Kernel. This in effect adds that amount of the original image into the resulting convolution kernel. This value is usually provided by the user as a percentage value in the 'convolve:scale' setting.</p> 543 544<p>The resulting effect is to convert the defined kernels into blended soft-blurs, unsharp kernels or into sharpening kernels.</p> 545 546<p>The format of the UnityAdditionKernelInfo method is:</p> 547 548<pre class="text"> 549void UnityAdditionKernelInfo(KernelInfo *kernel, const double scale ) 550</pre> 551 552<p>A description of each parameter follows:</p> 553 554<dd> 555</dd> 556 557<dd> </dd> 558<dl class="dl-horizontal"> 559<dt>kernel</dt> 560<dd>the Morphology/Convolution kernel </dd> 561 562<dd> o scale: </dd> 563 564<pre class="text"> 565 the given kernel. 566</pre> 567 568<p></dd> 569</dl> 570<h2><a href="http://nextgen.imagemagick.org/api/MagickCore/morphology_8c.html" id="ZeroKernelNans">ZeroKernelNans</a></h2> 571 572<p>ZeroKernelNans() replaces any special 'nan' value that may be present in the kernel with a zero value. This is typically done when the kernel will be used in special hardware (GPU) convolution processors, to simply matters.</p> 573 574<p>The format of the ZeroKernelNans method is:</p> 575 576<pre class="text"> 577void ZeroKernelNans (KernelInfo *kernel) 578</pre> 579 580<p>A description of each parameter follows:</p> 581 582<dd> 583</dd> 584 585<dd> </dd> 586<dl class="dl-horizontal"> 587<dt>kernel</dt> 588<dd>the Morphology/Convolution kernel </dd> 589 590<dd> </dd> 591</dl> 592</div> 593 <footer class="magick-footer"> 594 <p><a href="../script/support.php">Donate</a> • 595 <a href="../script/sitemap.php">Sitemap</a> • 596 <a href="../script/links.php">Related</a> • 597 <a href="../script/architecture.php">Architecture</a> 598</p> 599 <p><a href="morphology.php#">Back to top</a> • 600 <a href="http://pgp.mit.edu:11371/pks/lookup?op=get&search=0x89AB63D48277377A">Public Key</a> • 601 <a href="../script/contact.php">Contact Us</a></p> 602 <p><small>© 1999-2016 ImageMagick Studio LLC</small></p> 603 </footer> 604</div><!-- /.container --> 605 606 <script src="https://ajax.googleapis.com/ajax/libs/jquery/1.11.3/jquery.min.js"></script> 607 <script src="http://nextgen.imagemagick.org/js/magick.php"></script> 608</div> 609</body> 610</html> 611